business intelligence

Future of BI: Top 10 business intelligence trends for 2024

As we step into the new year, the world of business intelligence (BI) is brimming with new possibilities. The familiar trends that once guided us are now evolving, giving rise to a future where data unfolds with fresh perspectives and unexplored potential.

It's no longer just a question of whether you need business analytics, but rather, how to find the best BI solution tailored to specific needs. By staying up to date with emerging trends, you’ll ensure your BI solution continues to provide the most value to your business.

1. Artificial intelligence

Artificial intelligence (AI) is ushering in a new era for business intelligence, fundamentally transforming how organizations extract value from their data. By leveraging advanced analytics, machine learning, and natural language processing, AI is enhancing the capabilities of BI tools, making data more accessible, insightful, and actionable. Here's how AI is reshaping the landscape of business intelligence:

  • Automated data preparation: Traditional BI involves manual data cleaning and preparation, consuming valuable time and resources. AI automates data cleansing, transformation, and integration, enabling data teams to prepare data faster and more accurately than before. Tools like ThoughtSpot Sage automatically generate suggested synonyms to optimize your search experience, enhancing the efficiency of data modeling and search functionalities.


  • Advanced predictive analytics: Historical data analysis provides insights into the past but may not be sufficient for predicting future trends. Machine learning algorithms within BI tools can analyze historical data to make predictions and identify patterns, helping you anticipate market trends, customer behavior, and potential risks. 

  • Personalized insights: AI-driven BI solutions can offer personalized insights tailored to individual user preferences and roles. By analyzing user behavior and historical interactions with data, AI can recommend relevant reports, highlight key metrics, and provide insights that are specifically curated for each user. This personalization enhances the user experience and ensures that you receive the most relevant information for your roles. 

  • Search and AI-assisted embedded analytics: Beyond personalized insights, embedding AI-Powered Analytics directly into user workflows ensures seamless integration, increasing user adoption, and boosting productivity. For instance, ThoughtSpot's innovative AI-driven search approach not only streamlines the process but also improves the efficiency and user-friendliness of deriving actionable insights, ultimately contributing to better decision-making across all business levels.

2. Data governance

In an era of data-driven decision-making, ensuring the integrity and security of data is paramount. Robust data governance practices are emerging as a critical business intelligence trend. Establishing and maintaining comprehensive data governance frameworks is crucial for addressing data quality, security, and compliance challenges. 

  • Regulatory compliance: With an increasing number of data regulations, organizations must adhere to strict compliance standards. Strong data governance ensures that BI processes align with regulatory requirements, minimizing the risk of legal and financial repercussions and ensuring the trust of your customers, which is crucial for the continued success of your business.

  • Security and privacy: Data breaches are a prevalent threat in today’s environment. Data governance plays a crucial role in strengthening the security of sensitive information. It involves implementing powerful access controls, encryption mechanisms, and authentication protocols to safeguard data from unauthorized access. Moreover, data governance ensures compliance with industry regulations and data protection laws, instilling confidence among stakeholders that their data is handled ethically and legally.

  • Data ownership and accountability: Data governance defines and assigns ownership for each dataset within an organization. This helps establish accountability for the quality and accuracy of the data. With clear lines of responsibility, you can mitigate the risk of data errors and inconsistencies, fostering a culture of accountability that permeates throughout the entire data lifecycle. Features like user roles and Verified Liveboards help users have confidence in their data.

  • Change management: As your business grows and technology evolves, you need a BI solution that grows with you and a change management plan that supports your success. This involves updating policies, procedures, and documentation to reflect changes in data sources, business processes, or regulatory requirements. Moreover, it's essential to consider the financial aspects of these changes. Implementing effective FinOps practices ensures that associated costs are optimized during these transitions. 

3. Data quality management

Amidst the surge of data, maintaining high data quality is non-negotiable. Emphasizing the importance of powerful data quality management practices, ensuring accurate, complete, and consistent data becomes foundational for reliable insights.

  • Data profiling and cleansing: Data quality management involves thorough data profiling to identify inconsistencies and errors. Automated data cleansing processes are then applied to rectify issues, ensuring that the data used for analysis is accurate and reliable.

  • Standardization and normalization: Standardizing and normalizing data formats and structures contribute to data consistency. Data quality management practices include enforcing standardization rules to ensure that data is presented in a uniform and easily interpretable manner.

  • Data lineage tracking: Understanding the origin and transformations applied to data is crucial for maintaining data quality. Data lineage tracking within BI tools allows organizations to trace the journey of data from source to analysis, ensuring transparency and accountability.

  • Continuous monitoring and improvement: BI platforms with integrated data quality features enable organizations to proactively identify and address data quality issues as they arise.

4. Data discovery and visualization

Data discovery and visualization tools are evolving to provide more intuitive and user-friendly interfaces. BI solutions that leverage innovative visualization techniques, augmented reality, and immersive analytics will enable all users to explore and understand data in a more impactful way. Here's how data discovery and visualization are reshaping the landscape of business intelligence:

  • Visual storytelling: Data visualization is essentially about data storytelling. Using charts, graphs, dashboards, and other elements, visuals allow you to represent data trends, patterns, and anomalies. This visual representation goes beyond traditional spreadsheets, allowing any user to quickly absorb information and make informed decisions without delving into the intricacies of raw data. Taking a storytelling approach enhances data communication. A notable application of this approach is found in the integration of ThoughtSpot with Google Slides. With auto-updated visuals, your weekly presentation will never go out of date—allowing you to focus on the data story rather than the data’s accuracy.


  • Interactivity and exploration: Data visualization tools are evolving to offer more interactive features, allowing you to explore data sets in a dynamic and personalized manner. Features like drill-downs and filters empower you to easily interact with visualizations, so all users can gain comprehensive insights.

data discovery and visualization
  • Accessibility for all users: Making data analysis accessible to a wider audience within an organization is the key aim of data discovery and visualization. The intuitive nature of these tools reduces the learning curve, allowing individuals across different departments to engage with and interpret data without the need for specialized training.

  • Integration with AI and machine learning: The future of data discovery and visualization is closely tied to advancements in artificial intelligence and machine learning. BI tools incorporate AI-driven features that automatically identify patterns, outliers, and correlations in the data, enhancing the speed and accuracy of visual analysis. Using advanced analytics powered by AI, data visualizations don’t just present historical insights—they also offer intelligent predictions.

5. Natural language processing (NLP)

Natural Language Processing (NLP) is a subfield of artificial intelligence that focuses on enabling computers to understand, interpret, and generate human language. BI tools are set to become more user-friendly empowering users to interact with data using everyday language, making analytics available to a broader audience. 

  • Conversational BI: The user-friendly nature of NLP-driven interfaces continues to increase user adoption of BI tools. The conversational analysis approach makes data discovery more approachable, attracting users who might have been hesitant to engage with traditional, query-based interfaces.

  • Automated report generation: With NLP, you can automate the generation of reports and dashboards based on queries. This streamlines the reporting process, saving time, and ensuring that you receive the information you need without having to manually create or customize reports. 

  • Enabling on-the-fly exploration: Through NLP, users can navigate data dimensions, refine queries, and foster a dynamic, exploratory approach to data analysis. Forward-thinking BI solutions, like ThoughtSpot, enhance this experience with its intuitive NLP, allowing users to uncover insights seamlessly through conversational queries and AI-augmented answers. 

6. Embedded analytics

Embedded analytics involves integrating BI capabilities directly into other applications, products, or workflows. As users look for ways to save time and work faster embedded analytics is becoming more important, as it enables organizations to seamlessly incorporate data insights into their day-to-day operations without switching between different tools.

  • Streamlined decision-making: The integration of analytics directly into operational applications empowers you to make informed decisions in real time. For example, within a customer relationship management (CRM) system, sales representatives can access predictive analytics, customer behavior patterns, and sales trends without leaving the CRM interface. This immediate access to insights enhances decision-making agility and effectiveness.

  • Enhanced user adoption: One of the significant advantages of embedded analytics is its ability to promote widespread adoption among users. Users are more likely to engage with data without the need for extensive training or fighting the experience cost of switching to standalone BI platforms. This user-friendly approach democratizes access to data, making analytics a natural part of daily workflows.

  • Reduced time-to-insight: Traditional BI processes often involve extracting data from source systems, transforming it, and then loading it into a separate analytics tool. Embedded analytics streamlines this process by allowing users to analyze data within the same application where it is generated or utilized. This reduction in time-to-insight is particularly valuable in fast-paced business environments.

  • Developer productivity: When it comes to embedding analytics into your product, developers have the distinct benefit of time savings. Instead of building a new BI experience, they can concentrate on building other, more important product features With ThoughtSpot Everywhere’s easily accessible analytics services, robust APIs, and flexible modeling language, developers can effortlessly tap into and utilize powerful analytical tools within the context of their applications.

To illustrate the impact of embedded analytics on organizational decision-making, consider Harri, a software company focused on empowering hospitality managers with data insights to help users navigate the “source and hire" process of employee recruitment. By tailoring their interactive data experiences to meet the unique needs of their three main user personas, Harri created a tier-based approach to data monetization

7. Mobile business intelligence

As the workforce becomes increasingly mobile, there is increasing demand for on-the-go business intelligence solutions. One of the future predictions is that BI platforms will prioritize mobile optimization, providing users with real-time access to data and analytics on their smartphones and tablets. 

  • On-the-go decision-making: Mobile BI enables decision-makers to access critical business insights anytime, anywhere. Whether in a board meeting, traveling, or working from home, stakeholders can make informed decisions promptly, reducing delays in response to market dynamics. ThoughtSpot's mobile feature takes this a step further by providing a user-friendly interface and responsive design, ensuring a seamless and efficient experience for users on various devices

  • Accessibility and convenience: The ubiquity of mobile devices ensures that key stakeholders have access to BI reports and dashboards at their fingertips. This accessibility enhances the convenience of data consumption, allowing users to stay informed without being tethered to a desktop or office setting.

  • Data security: Security features are paramount in Mobile BI solutions to safeguard sensitive business information. Advanced encryption, secure authentication methods, and remote data wipe capabilities ensure that data remains confidential even when accessed on mobile devices.

  • Offline access: Recognizing that connectivity may not always be guaranteed, Mobile BI solutions often include offline access capabilities. You can download essential reports and dashboards for offline viewing, ensuring access to critical insights even in areas with limited or no network connectivity.

8. Invisible business intelligence

Invisible BI represents a paradigm shift in the way organizations leverage business intelligence. Unlike traditional BI approaches where users actively seek out and interact with analytics tools, Invisible BI focuses on delivering insights effortlessly. This transformative approach emphasizes making data-driven insights an inherent part of decision-making processes, reducing the need for users to switch between different applications. Here's how invisible business intelligence is reshaping the landscape of business intelligence:

  • Proactive alerts: Invisible BI relies on proactive alerts to notify users of significant changes or trends in the data. Instead of users actively monitoring dashboards or reports, the system intelligently identifies noteworthy events and sends alerts, ensuring that decision-makers are promptly informed without having to seek out information. These alerts can be seamlessly integrated into communication platforms like Slack or email, providing real-time notifications within your existing workflow.




  • Automated suggestions: Another hallmark of Invisible BI is the incorporation of automated suggestions. The integrated intelligence system, based on machine learning algorithms, can recommend relevant analyses, visualizations, or next steps based on user behavior and historical data patterns. This integration extends to platforms like Google Workspace, seamlessly suggesting insights within collaborative documents or emails, streamlining decision-making, and guiding users toward actionable insights effortlessly.

  • Context-aware recommendations: Invisible BI is designed to be context-aware, tailoring recommendations and insights based on the user's role, responsibilities, and the specific context of their current activities. By understanding the user's context, the system delivers insights that are directly relevant to their immediate needs, enhancing the overall usability and effectiveness of BI tools.

  • Integration with workforce applications: Invisible BI seamlessly integrates with other workforce applications and business tools such as Google Workspace, Slack, Salesforce, and more. This ensures that data-driven insights are not isolated but become an integral part of the broader organizational ecosystem. 

9. Self-service business intelligence

Self-service business intelligence represents a paradigm shift in how organizations approach data analytics. A key aspect of this shift is the empowerment of business users to independently generate reports and derive insights without heavy reliance on data teams. Some BI tools are actively evolving to enhance self-service capabilities, allowing users to create reports, conduct analyses, and derive insights with minimal reliance on IT support.

  • User-friendly interfaces: Self-service BI tools prioritize intuitive and user-friendly interfaces that ensure individuals from various departments can navigate the tools easily, regardless of their technical expertise. The emphasis is on making complex data analytics accessible to a broader audience. Notably, ThoughtSpot excels in this regard, featuring an interface designed for simplicity and efficiency. You can effortlessly explore and analyze data, promoting a more inclusive and democratized approach to business intelligence.

  • Drag-and-drop functionality: User-friendly BI features like drag-and-drop capabilities empower users to create reports and visualizations effortlessly. This approach allows you to build compelling insights without the need for extensive technical skills, making the process more accessible to everyone. For example, ThoughtSpot's drag-and-drop features enable users to effortlessly create meaningful visualizations, putting the power of data analysis in the hands of business users.

  • Ad-hoc querying: Self-service BI tools provide ad-hoc querying capabilities, allowing users to ask spontaneous questions about their data. This flexibility empowers users to explore data on the fly and uncover insights that might not be addressed by pre-built reports. With ThoughtSpot's ad-hoc querying, users can engage with their data dynamically, gaining deeper insights on-demand.

🔍See how companies are generating 289% ROI and $6.3M in value with ad hoc querying from ThoughtSpot.

10. Collaborative business intelligence

In the collaborative era, business intelligence is no longer confined to individual users. collaborative BI emphasizes sharing insights across teams and departments with ease. Users can annotate, share, and discuss findings within the context of the data, fostering a collaborative and data-driven culture. To further enhance this collaborative experience, consider exploring solutions that seamlessly bring your data and business teams together.

  • Real-time collaboration: Promoting real-time interaction, collaborative BI platforms empower multiple users to concurrently engage with the same dataset. This dynamic environment enables teams to collectively analyze data, share perspectives, and contribute to the decision-making process in a synchronized manner.

  • Commenting and annotations: To enhance communication and contextual understanding, collaborative BI platforms often feature commenting and annotation capabilities. You can add comments directly within the BI tool, providing insights, asking questions, or offering clarifications. This interactive element streamlines communication and reduces the need for separate discussions outside the BI environment. In addition to traditional commenting and annotations, with ThoughtSpot’s innovative note tiles, you can provide important directions for how to read or use a specific visualization. 

  • User permissions and access control: Collaborative BI emphasizes granular control over user permissions and access. Administrators can define who has access to specific datasets, dashboards, or reports, ensuring that sensitive information is only available to authorized personnel. This security measure is crucial for maintaining data integrity and compliance.

  • Discussion threads: Within the framework of collaborative BI platforms, discussion threads are often integrated with specific datasets or reports. This feature enables team members to participate in data-driven conversations, share observations, and collaboratively derive meaningful conclusions.

Charting the path forward

The future of business intelligence is exciting, driven by the convergence of technology advancement and a growing awareness of data’s value. Organizations that embrace these trends will be better positioned to navigate the complexities of an AI-powered world and gain a competitive edge. 

As we move forward, the synergy between human intelligence and technology will redefine how we extract insights from business intelligence. ThoughtSpot is at the forefront of this innovation, blending natural language search, user-friendly Liveboards, and AI-Powered Analytics with the tools data teams love, like AI-assisted data modeling, advanced government and security controls, and seamless integrations with your existing cloud data environments. By prioritizing the experience for all users, we’re setting a new standard for scalable self-service analytics

Ready to revolutionize your data analytics experience? See the future of business by joining a free demo of ThoughtSpot.